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Re: Hard vs. Soft or Hard + Soft KR, Re: neural networks being purported as KR?

From: Paola Di Maio <paola.dimaio@gmail.com>
Date: Mon, 29 Jul 2019 16:52:57 +0800
Message-ID: <CAMXe=Sp=Js88UUkczMyHTxP7y60cp5N+tbH-8La6pozg+EmnVA@mail.gmail.com>
To: "Stephen D. Williams" <sdw@lig.net>
Cc: SW-forum <semantic-web@w3.org>
Stephen

thank you, I am thinking along those lines and we could have a discussion
about
hard vs soft KR

My challenge today is to find a reference for ''non concept'
(as used here  Concept Recognition with Convolutional Neural Networks to
Optimize Keyphrase Extraction Andreas Waldis , Luca Mazzola(B) , and
Michael Kaufmann School of Information Technology, Lucerne University of
Applied Sciences, 6343 Rotkreuz)

The notion of non concept seems to be increasingly used in ANN to contrast
'the notion of concept in traditional KR,

But I cannot find out if this has been formally defined *I doubt it?

So no only a lot of peer reviewed scholarly articles make false statements
(say things which are not true) but also reference notions which have never
been formally defined (most of this vague research seems to be happening in
Switzerland)

This is not science, is it?

Together with other things I am not figuring out, I tind this unsettling
and adding
to uncertainties


PDM

On Mon, Jul 29, 2019 at 11:51 AM Stephen D. Williams <sdw@lig.net> wrote:

> Thanks, great info!
>
> Knowledge graphs, semantic web, and other explicit knowledge
> representations are important.  But, as you point out, they currently don't
> capture everything, especially not probability and complex rational but not
> simple logic relationships.  ML and related enabled training, capture, and
> usage of this aspect of information, but most or all structure is opaquely
> inferred and usually shallow.  Somehow we need all of the capabilities of
> both in a merged or married structure.  I totally believe in the value of
> both.
>
> My observation is that we almost certainly reason with structure, logic,
> math in general, language, etc. on top of ML-like mechanisms (albeit far
> more varied, numerous, and expansive), and we fill in the gaps all the time
> with ML-like sub-rational or at least sub-logical reasoning.  Most of us
> most of the time reason largely in this sub-logical reasoning mode, often
> interpreting, viewing, or fitting those thoughts and conclusions to formal
> structures afterwards or in a trailing overlapping fashion.
>
> https://www.ted.com/playlists/384/how_your_brain_constructs_real
>
> As a software & security architect and now robotic hardware designer, I
> never think in formal equations or systems, yet I automatically explore
> creative solution spaces with many, many constraints and patterns in mind.
> I can often construct equations of various types to summarize and
> communicate.  I now find this to be true of business and interpersonal
> situations (various concerns, constraints, interests, conflicting aspects)
> and mechanical systems.  For instance, this is a summary of part of the
> robotic actuator I've been working on:
>
> I1 * r/R * CVT - I2 * R/2 = RIVT
>
> I never could have found that solution through explicit logical deduction,
> yet the process of working through and mentally testing possibilities is
> very concrete and not mysterious at all.  How do you experience your
> knowledge, thinking, problem solving, and creativity?  Do you feel strongly
> and deeply aware of it or is it mysterious and opaque?  Do you visualize a
> little, a lot, or not at all?  (We're recently realized that something like
> 10% of people cannot visualize anything, ever, yet a number of animators
> are among those!  They seem to use drawing as external working memory /
> thought space.)
>
> We already have ML solutions that are able to learn far more than simple
> table lookups.  Creating a program to play a video game using strictly
> explicit logic, KG methods would be challenging while we have straight
> foward ML solutions already:
>
>
> https://thenextweb.com/artificial-intelligence/2018/08/23/researchers-gave-ai-curiosity-and-it-played-video-games-all-day/
>
> https://towardsdatascience.com/an-exploration-of-neural-networks-playing-video-games-3910dcee8e4a
>
> It seems promising to extrapolate from these for some distance.  Our
> challenge is to find a intersection method that allows us to combine these
> two mostly independent paths to create really interesting solutions.  My
> sense is that there are two possibilities:
>
> We get an interface method working that allows an ML system to access
> computational algorithms / KG / semantic / logical knowledge through a kind
> of port, portal, map, or similar.
>
> Or that we evolve the ML systems to the point where they can embody, in a
> reasonably efficient and accurate way, this computational / KG / semantic /
> logical knowledge along with the more probabilistic, analog networked
> knowledge.
>
> Or both.
>
>
> Rather than connectionist vs. reductionist etc., perhaps these are useful
> labels:
>
> Hard knowledge: computational algorithms / KG / semantic / logical
> knowledge
>
> Soft Knowledge: ML, vision, everything connectionist or probabilistic
>
> Or sharp vs. round or curved or smooth.
>
> Stephen
>
> On 7/26/19 9:02 AM, ProjectParadigm-ICT-Program wrote:
>
> Thank you Paola for pointing this out.
>
> Again I must beat the drum.
>
> Knowledge is much more than extracting structure from facts and data. If I
> just recall that the collection of facts is subject to the uncertainty
> principle, any structure deduced cannot be complete, and the application of
> free will, and/or axiom of choice create a dichotomy, knowledge is much
> more.
>
> We are limited by our sensory apparatus, our hard wiring in our human
> brain, including the shortcuts made when processing visual data, and the
> limitations of natural language.
>
> I agree that knowledge reasoning should be fairly straightforward, but
> making the jump from KR to knowledge itself implies we come up with some
> consistent many worlds modeling scheme in which the virtual, mathematical
> and (many interpretations of) the physical world coexist, reconciling
> incompleteness, uncertainty principle, sensory limitations and application
> of free will and choice.
>
> A convergence of efforts by string theorists, researchers in human brain
> cognitive and biological structure fields, theoretical physicists and
> mathematicians working on finite groups, category theory, algebraic
> topology and logical structures for consistent super theories, and an odd
> mix of linguists and philosophers (including Buddhists) is doing just that.
>
> But they are far from a consensus.
>
> The point I am trying to make is that KR is more than semantics and
> ontologies and knowledge graphs, graphs, category theory diagrams and
> Feynmann diagrams and any other visualization tools we use.
>
> The implicate order David Bohm theoreticized underlying quantum reality
> and the reality of our physical world, cannot be captured by some mix of
> formal logic, semantic structures, ontologies or computable frameworks.
>
> And we we want someday A(G)I to be able to grasp human knowledge in
> general, we must create a growth path towards formal structures which have
> meta-layers above (knowledge) graphs, formal logic and ontologies.
>
> Mathematically speaking, using formal logic, ontologies and generalized
> graphs is necessary but insufficient for this general formal structure.
>
> And now I must add that deep learning and machine learning also fall short
> in terms of KR'
>
> If we let computer scientists, logicians, mathematicians and software
> engineers try to come up with KR which is fit for the AI we envision we
> will need for future applications we will fail miserably.
>
> We need neuroscientist, and specialists in the field of cognitive
> sciences, biologists and even psychologists, and philosophers and
> physicists to help us complete the general framework for knowledge, and to
> establish which parts can be effectively captured in a formal fashion,
> which provide suitable technologies and tools for KR.
>
> Mike Bergman did a nice expose on knowledge graphs at:
> A Common Sense View of Knowledge Graphs
> <http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/>
>
>
>
>
> A Common Sense View of Knowledge Graphs
>
> This article, based on a comprehensive history and definitions of the
> concept, provides a common-sense view of h...
> <http://www.mkbergman.com/2244/a-common-sense-view-of-knowledge-graphs/>
>
> But historically even mandalas qualify as knowledge graphs, in a very
> stylized way. And they can be used to visualize very complex mathematical
> structures without the use of edges or arrows, thus removing the time
> component associated with the transition the edge or arrow represents,
> making knowledge representation in a time-independent fashion possible.
>
>
>
> Milton Ponson
> GSM: +297 747 8280
> PO Box 1154, Oranjestad
> Aruba, Dutch Caribbean
> Project Paradigm: Bringing the ICT tools for sustainable development to
> all stakeholders worldwide through collaborative research on applied
> mathematics, advanced modeling, software and standards development
>
>
> On Thursday, July 25, 2019, 11:52:23 PM ADT, Paola Di Maio
> <paola.dimaio@gmail.com> <paola.dimaio@gmail.com> wrote:
>
>
> Sorry to bang on this topic, but its the task at hand at the moment
>
> I just found an article, which is good scientific survey then  purports NN
> as a type of KR
> (casually sneaks in NN as the latest KR)
>
> This is published in a Springer peer reviewed publication and my makes all
> of my hairs stand up on my head
>
> This is the kind of rubbish that without further qualification is being
> passed down
> as the latest research, and  which the future generations of AI scientists
> are being fed-
>
> wonder if anyone else has a problem with this proposition
> (sign of the times?)
> I am doing my best within my means to identify and contain this peril
>
> Article
> https://link-springer-com.nls.idm.oclc.org/article/10.1007/s00170-018-2433-8
>
>
> A survey of knowledge representation methods and applications in machining
> process planning
>
> The machining process is the act of preparing the detailed operating
> instructions for changing an engineering design into an end product, which
> involves the removal of material from the part. Today, machining ...
>
> Xiuling Li, Shusheng Zhang, Rui Huang… in The International Journal of
> Advanced Manu… (2018)
>
>
>
>
> --
>
> *Stephen D. Williams*
> Founder, Yebo, VolksDroid, Blue Scholar
> 650-450-8649 | fax:703-995-0407 | sdw@lg.net <sdw@lig.net> |
> https://HelloYebo.com | https://VolksDroid.org | https://BlueScholar.com |
> https://sdw.st/in
>
Received on Monday, 29 July 2019 08:54:01 UTC

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